Magnetic resonance images suffer from an intensity inhomogeneity known as a “bias field” caused by the placement of image coils in the scanner. As a result of this bias field, some parts of the acquired image (typically the center) are darker than the rest of the image. To equalize image intensity, a normalization field is typically applied to the image data during post-processing. The normalization field can be estimated in various ways, e.g. from the images themselves, or from pre scan data. However, because this normalization field is applied to the entire image, it increases both the signal and noise of the data. Thus, after normalization, denoising techniques must be applied to the data.
Although conventional denoising techniques exist, they have drawbacks which produce suboptimal results. For example, many conventional techniques are not adaptive to variable noise levels that may be present in normalized image data. As a result, these techniques overly smooth low noise regions. Additionally, the application of conventional techniques over a high number of iterations introduces image blurring which distorts certain anatomical structures such as liver vessels.